JP2017157049A - Topography classification system and topography classification method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a topography classification system and a topography classification method capable of solving problems in prior arts, namely avoiding variation in technicians, thereby classifying topographies into a topography having characteristics, for extracting a topography variation line by objective determination.SOLUTION: The topography classification system is a system for classifying kinds of topographies into each kind of a topography using a topography model, and comprises: topographic amount calculation means; sample area extraction means; attention line setting means; scatter chart creation means; topography classification condition setting means; and characteristic topography classification means. The scatter chart creation means arranges an average inclination amount and an inclination standard deviation of a unit compartment in a sample area, on a two-axis plane formed of an average inclination amount axis and an inclination standard deviation axis, for creating a scatter chart. The characteristic topography classification means classifies a unit compartment in which combination of the average inclination amount and the inclination standard deviation is at an upper part or a lower part of a threshold function, as a characteristic unit compartment.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a technique for classifying characteristic terrain using a terrain model, and more specifically, an average inclination amount and a standard deviation of an inclination amount (hereinafter referred to as “inclination” for each unit section constituting the terrain model). It is related to a system and a method for classifying terrain based on the average inclination amount and the inclination standard deviation.

  Japan is prone to damage from slope disasters such as landslides and slope failures because of the large percentage of mountainous land in the country and heavy rain. In particular, once a landslide or deep landslide occurs, it can be extremely damaging. In order to prevent (or reduce) damage caused by such slope disasters, countermeasures have been implemented on the slopes that have caused the disaster, and evacuation plans have been formulated in the event of a disaster.

  By the way, it is necessary to identify a slope that causes a disaster, whether it is a slope countermeasure work or a refuge plan for residents. Conventionally, terrain where landslides are likely to occur (hereinafter referred to as “landslide terrain”) and terrain where slope failures are likely to occur (hereinafter referred to as “collapsed terrain”) have been extracted by engineers having specialized skills. . At that time, it is difficult to immediately identify landslide or collapse terrain from a huge range of terrain. First, list the candidate terrain using the topographic features as clues, and examine the candidate terrain in detail to understand the landslide terrain and It was common to extract the collapsed terrain.

  In order to list candidates for landslide topography and collapse topography, it is effective to focus on topography where the slope slope changes suddenly, and such topography changes from a gentle slope to a steep slope when viewed from above the slope. The “Transit Line” and the “Relax Line” that changes from a steep slope to a gentle slope are known. Moreover, paying attention to a certain cross section, a point constituting the transition line in the cross section may be referred to as a “transition point”, and a point constituting the transition line may be referred to as a “transition point”. In addition, here, the generic name of the rapid line and the gentle line is referred to as “terrain change line”, and the generic term of the rapid point and the gentle point is referred to as “terrain change point”.

  Like landslides and collapsed terrain, topographic change lines and topographic change points have also been extracted by experts. The topographic change lines are extracted according to the past experience together with the knowledge possessed by the specialists, but the results may differ from engineer to engineer. It was. As described above, the extraction of the terrain change line or the like is performed in order to list candidate terrain such as the landslide terrain, and the extraction failure of the terrain change line or the like means the extraction failure of the landslide topography or the like.

  In order to avoid such an artificial variation, in other words, for the purpose of obtaining an objective result, efforts have been made to extract features of the terrain mechanically. Patent Document 1 proposes a technique for automatically obtaining a landslide direction by giving a landslide block (planar shape) to a topographic map.

JP 2005-164421 A

  However, up to now, including Patent Document 1, no technology has been proposed to extract the terrain change line (or terrain change point) such as a rapid line or a gradual line mechanically (automatically). There was a strong demand for such a technology.

  The object of the present invention is to solve the problems of the prior art, that is, to avoid variation by engineers, that is, to extract terrain change lines by objective judgment, so that terrain that can classify characteristic terrain It is to provide a classification system and a terrain classification method.

  The present invention focuses on the point of classifying characteristic terrain based on the average slope amount and slope standard deviation obtained for each unit section constituting the terrain model. It is an invention made.

  The terrain classification system of the present invention is a system that classifies the type of terrain using a terrain model, and includes a terrain amount calculating means, a sample area extracting means, a notice line setting means, a scatter diagram creating means, a terrain classification condition setting means, Features terrain classification means. Here, the terrain model includes a large number of unit sections obtained by dividing a predetermined area into planes, and elevation values included in the unit sections. The terrain amount calculation means calculates the average inclination amount and the inclination standard deviation for the unit section, and the sample area extraction means extracts the sample area by paying attention to the terrain feature in the predetermined area by the operation of the operator. It is. The attention line setting means sets the attention line that intersects the boundary of the sample area by the operator's operation, and the scatter diagram creation means calculates the average inclination amount and inclination standard deviation of the unit sections in the sample area as the average inclination. It is arranged on a biaxial plane consisting of a quantity axis and a tilt standard deviation axis to create a scatter diagram. The terrain classification condition setting means sets a threshold function with variables of the average slope amount and the slope standard deviation based on the scatter diagram. The feature terrain classification means has a combination of the average slope amount and the slope standard deviation as a threshold function. The unit section above or below is classified as a characteristic unit section. The average inclination amount is calculated by each unit section and a plurality of unit sections around the unit section, and the inclination standard deviation is obtained based on the plurality of peripheral inclination amounts.

  The terrain classification system of the present invention may further include terrain change line setting means. The terrain change line setting means sets an area where a plurality of feature unit sections classified by the feature terrain classification means are set as a feature area, and sets the boundary of the feature area as a transition line or a rapid line.

  The terrain classification method of the present invention is a method of classifying the type of terrain using a terrain model, a terrain quantity calculation step, a sample area extraction step, a attention line setting step, a scatter diagram creation step, a terrain classification condition setting step, It has a feature terrain classification process. In the terrain amount calculation step, the average inclination amount and the inclination standard deviation are obtained for the unit section, and in the sample region extraction step, the sample region is extracted by paying attention to the feature of the terrain among the predetermined regions by the operation of the operator. In the attention line setting process, an attention line that intersects the boundary of the sample area is set by the operator's operation, and in the scatter diagram creation process, the average inclination amount and inclination standard deviation of the unit sections in the sample area are expressed as the average inclination amount axis. And a scatter diagram is created by arranging on a biaxial plane composed of the tilt standard deviation axis. In the terrain classification condition setting process, a threshold function with the average slope and slope standard deviation as variables is set based on the scatter diagram. In the feature terrain classification process, the combination of the average slope and slope standard deviation is above the threshold function. Alternatively, the lower unit section is classified as a feature unit section. The average inclination amount is calculated by each unit section and a plurality of unit sections around the unit section, and the inclination standard deviation is obtained based on the plurality of peripheral inclination amounts.

The terrain classification system and the terrain classification method of the present invention have the following effects.
(1) A characteristic terrain can be classified by objective judgment while avoiding variation by engineers. As a result, it is possible to objectively extract the terrain change line, and thus prevent omission of landslide topography and collapse topography.
(2) Since work by humans can be largely omitted, the work cost can be reduced, and landslide topography and the like can be extracted more quickly than before.

The block diagram which shows the landform classification system of this invention. The flowchart which shows the main processes (process) for setting the conditions for extracting a landform change point. The block diagram which shows a condition setting means. The model figure explaining a topographic model and a unit block. The model figure explaining the attention line set with respect to the sample area | region. (A) Model diagram explaining the calculation method of the inclination amount in the X-axis direction by the primary trend surface analysis, (b) Model diagram explaining the calculation method of the inclination amount in the Y-axis direction by the primary trend surface analysis. (A) is a scatter diagram for the attention line Line1 set for the sample region shown in FIG. 5, (b) is a scatter diagram for the attention line Line2 set for the sample region shown in FIG. 5 is a scatter diagram for the attention line Line3 set for the sample region shown in FIG. 5, (d) is a scatter diagram for the attention line Line4 set for the sample region shown in FIG. 5, and (e) is shown in FIG. The scatter diagram in the attention line Line5 set with respect to the sample area | region. The flowchart which shows the main processes (process) for extracting a feature unit division with respect to a predetermined area | region. The block diagram which shows a feature area extraction means.

  An example of the terrain classification system and the terrain classification method of the present invention will be described with reference to the drawings.

1. Overall Overview FIG. 1 is a block diagram showing a terrain classification system 100 of the present invention. As shown in this figure, the terrain classification system 100 includes a condition setting unit 110 and a feature area extraction unit 120, and can also include an output unit 130 such as a printer or a display. The condition setting unit 110 extracts conditions (hereinafter, “terrain classification conditions”) for extracting unit sections (hereinafter referred to as “feature unit sections”) that constitute characteristic landform areas (hereinafter referred to as “characteristic areas”). The feature region extraction means 120 extracts feature unit sections based on the terrain classification conditions. The landform classification system 100 of the present invention may be configured using a computer that executes a program. Hereinafter, the condition setting unit 110 and the feature area extraction unit 120 will be described in detail.

2. Condition Setting Unit FIG. 2 is a flowchart showing main processing (steps) for setting the terrain classification condition, and FIG. 3 is a block diagram showing the condition setting unit 110. The middle column of FIG. 2 shows the processing (process) to be performed, the left column shows the input information necessary for the processing (step), and the right column shows the output information generated from the processing (step). Is shown. Hereinafter, the condition setting means 110 will be described with reference to these drawings.

(Terrain model)
FIG. 4 is a model diagram for explaining the terrain model and the unit section. As shown in this figure, the terrain model TM is a mesh-like model obtained by dividing a predetermined area AR into planes, and the unit block BL is a small area obtained as a result of dividing into planes. In other words, the terrain model TM is composed of a large number of unit sections BL. For example, it can be seen that the broken line area on the left side of FIG. 4 is composed of 24 unit sections BL. In addition, as shown also in this figure, an identification number (BL323 to BL824 in the figure) is often given to each unit section BL. The topographic model TM is given plane coordinates (or latitude and longitude), and usually plane coordinates and the like are given to lattice points of the unit block BL.

  Typical examples of such a terrain model TM are DEM (Digital Elevation Model) and DSM (Digital Surface Model). The DEM, which is also referred to as the ground surface model, is given altitude values to the individual unit sections BL constituting the topographic model TM, while the DSM, also referred to as the surface layer model, is covered with the individual unit sections BL constituting the topographic model TM. Elevation values such as are given. These elevation values are generally assigned to the center point or grid point of the unit block BL. In FIG. 4, the predetermined area AR is divided into a substantially square lattice and the shape of the unit section BL is also substantially square. However, the shape of the unit section BL is not limited to this, but may be any shape such as a rectangle, a rhombus, or an oval. Further, the shape and size of each unit section BL can be changed.

  The terrain model TM is stored in the terrain model storage unit 111 shown in FIG. 3 and read by the terrain model reading unit 112 (Step 101: FIG. 2).

(Sample area)
Here, the sample area is an area characterized by terrain (especially gradient change), and is selected by an operator (operator). As the sample region, for example, a gentle slope or a steep slope is typically selected, and may be further subdivided according to the scale (area) in addition to these features. Note that only one type of sample region may be extracted for the predetermined region AR from which the feature region is to be extracted, or two or more types may be extracted. When two or more types of sample regions are extracted, the terrain classification conditions are set as many as the number of attention lines (described later) set in each sample region.

  The sample area is extracted by the operator operating the sample area extracting means 113 shown in FIG. 3 (Step 102: FIG. 2). Specifically, the operator may specify a predetermined area using a pointing device such as a mouse while checking the terrain model TM displayed on the display unit 130.

  When the sample region is extracted, the attention line is set (Step 103: FIG. 2). FIG. 5 is a model diagram for explaining the attention line set for the sample area SA. As shown in this figure, the attention line is set so as to intersect with the boundary of the sample area SA. In FIG. 5, five attention lines (Line 1 to Line 5) for one sample area SA are shown, but the number of attention lines can be arbitrarily set, and the boundary between the sample area SA and the attention line The crossing angle can also be arbitrarily selected. This attention line is set by the operator operating the attention line setting means 114 shown in FIG. 3. Specifically, the operator checks the terrain model TM and the sample area SA displayed on the display means 130, and the operator moves the mouse. The attention line may be designated using a pointing device.

(Average slope)
When the attention line is set for the sample area SA, the average inclination amount I is obtained for the unit block BL on the attention line. Usually, there are a plurality of unit sections BL on the attention line, and the average inclination amount I is obtained for each of the unit sections BL. Moreover, it is good to obtain | require the average inclination amount I with respect to the unit division BL concerning all the set attention lines. The unit section BL on the attention line can include a unit section BL within a predetermined distance from the attention line in addition to the unit section BL including a part of the attention line.

  The average inclination amount I is calculated as an inclination amount by primary trend surface analysis. Specifically, the average inclination amount I is calculated by using a unit partition BL to be calculated and a plurality of unit partitions BL around it. Here, in order to avoid confusion, the unit partition BL for which the average inclination amount I is calculated is referred to as “target unit partition BLa”, and the unit partition BL around the target unit partition BLa is referred to as “peripheral unit partition BLs”. .

  FIGS. 6A and 6B are model diagrams for explaining a method of calculating an inclination amount by primary trend plane analysis, where FIG. 6A shows an inclination amount in the X-axis direction, and FIG. 6B shows an inclination amount in the Y-axis direction. In this figure, a part of the terrain model TM arranged on the plane composed of the X axis and the Y axis is shown, and the shaded portion at the center is the target unit section BLa, and the surrounding portions are the peripheral units. It is set as a section BLs.

  In calculating the average inclination amount I of the target unit section BLa, first, a range of the peripheral unit section BLs used for the calculation (hereinafter referred to as “window region”) is set. For example, in FIG. 6, a total of 24 peripheral unit sections BLs are set with a window area of 5 (X-axis direction) × 5 (Y-axis direction) around the target unit section BLa. Note that the window region can be automatically generated based on a predetermined range (Nx × Ny), or can be set based on the range (Nx × Ny) input each time.

  When the window area can be set, the “peripheral inclination amount” is calculated using the target unit section BLa and the peripheral unit section BLs. A distance along the axial direction (hereinafter referred to as “axial distance”) is used for calculating the peripheral inclination amount. For example, in FIG. 6A, the X-axis component (the length projected on the X-axis) of the distance from the target unit section BLa (center) to the peripheral unit section BLs (center) is the X-axis distance, and the peripheral unit section BLs11. The X-axis distance between .about.BLs51 and the peripheral unit sections BLs15 to BLs55 is Lv1, and the X-axis distance between the peripheral unit sections BLs12 to BLs52 and the peripheral unit sections BLs14 to BLs54 is Lv2. Since the X-axis distance of the peripheral unit sections BLs13 to BLs53 is 0, these peripheral unit sections BLs are not used for calculating the peripheral tilt amount in the X-axis direction.

  Similarly, in FIG. 6B, the Y-axis component (the length projected on the Y-axis) of the distance from the target unit section BLa (center) to the peripheral unit section BLs (center) is the Y-axis distance, and the peripheral unit section The Y-axis distance between BLs11 to BLs15 and the peripheral unit sections BLs51 to BLs55 is Lh1, and the Y-axis distance between the peripheral unit sections BLs21 to BLs25 and the peripheral unit sections BLs41 to BLs45 is Lh2. Since the Y-axis distance of the peripheral unit sections BLs31 to BLs35 is 0, the peripheral unit sections BLs are not used for calculating the peripheral tilt amount in the Y-axis direction.

  The difference between the elevation values of the target unit section BLa and the peripheral unit section BLs divided by the axial distance shown in FIG. 6 is calculated as the “component inclination amount”. Note that, for the same peripheral unit section BLs, two component inclination amounts, that is, an X component inclination amount calculated by the X-axis distance and a Y component inclination amount calculated by the Y-axis distance are obtained (however, the peripheral unit) The sections BLs13 to BLs53 and the peripheral unit sections BLs31 to BLs35 are obtained with only one component inclination amount). Therefore, the combined value of the X component inclination amount and the Y component inclination amount (square root of the sum of squares) may be used as the peripheral inclination amount of the peripheral unit section BLs. When a plurality (24 in FIG. 6) of peripheral inclination amounts obtained for the target unit section BLa are obtained, the average inclination amount I of the target unit section BLa is calculated (Step 104: FIG. 2). The average inclination amount I is a value obtained based on the peripheral inclination amount, and can be calculated by various methods such as an arithmetic average, an arithmetic average, or a weighted average weighted by an axial distance. .

(Inclination standard deviation)
When the peripheral inclination amount is obtained, the inclination standard deviation σ is calculated in addition to the average inclination amount I (Step 105: FIG. 2). This inclination standard deviation σ is a value calculated as a standard deviation of a plurality (24 in FIG. 6) of peripheral inclination amounts obtained for the target unit section BLa.

(Amount of landform)
The average inclination amount I and the inclination standard deviation σ (hereinafter collectively referred to as “terrain amount”) are calculated by the terrain amount calculation means 115 shown in FIG. Note that the amount of topography is repeatedly calculated for as many (or all) unit sections BL as possible on all set attention lines (FIG. 2).

(Scatter plot)
When the topographic amount of the unit block BL in the sample area is obtained, the slope standard deviation σ is the vertical axis and the average slope amount I is the horizontal axis (or the average slope amount I is the vertical axis and the slope standard deviation σ is the horizontal axis). A scatter diagram is created in which the topographic amount of each unit section BL is arranged on the coordinate axis plane (Step 106: FIG. 2). In other words, a scatter diagram is obtained by plotting a combination of the average inclination amount I and the inclination standard deviation σ on the biaxial coordinate plane as coordinates. This scatter diagram can be created for each attention line. FIG. 7 is a scatter diagram created for each of the five attention lines set for the sample area SA shown in FIG. 5, where (a) is a scatter diagram for the attention line Line1, and (b) is for the attention line Line2. A scatter diagram, (c) is a scatter diagram on the attention line Line 3, (d) is a scatter diagram on the attention line Line 4, and (e) is a scatter diagram on the attention line Line 5. The scatter diagram is created by the scatter diagram creation means 116 shown in FIG.

(Threshold function)
When a scatter diagram is obtained, a threshold function is set (Step 107: FIG. 2). This threshold function is a function having the average inclination amount I and the inclination standard deviation σ as variables, and is set based on the point group (that is, the topographic amount) scattered on the scatter diagram. Accordingly, when the threshold function is drawn on the coordinate axis plane composed of the average inclination amount I and the inclination standard deviation σ, it is represented as a continuous straight line or curve. In FIG. 7, the threshold functions are all represented as straight lines. For this threshold function, for example, a specialized engineer can determine a boundary line that bisects a point cloud on the scatter diagram roughly vertically (or left and right), and this boundary line can be set as the threshold function.

  The threshold function is a unit that indicates the topographic amount above (or below) the threshold function because it can be said to be a boundary line that separates the topographically characteristic sample area from the non-topographical land in consideration of variation in the average slope amount I. The section is estimated as a feature unit section. Whether it is above or below the threshold function is determined according to the characteristics of the sample area (slow slope or steep slope). Therefore, this threshold function is the terrain classification condition, which is set by the terrain classification condition setting means 117 shown in FIG. 3 and stored in the terrain classification condition storage means 118.

3. Feature Area Extraction Unit FIG. 8 is a flowchart showing main processes (steps) for extracting feature unit sections from the predetermined area AR, and FIG. 9 is a block diagram showing the feature area extraction unit 120. . The middle column of FIG. 8 shows a process (process) to be performed, the left column shows input information necessary for the process (process), and the right column shows output information generated from the process (process). Is shown. Hereinafter, the feature region extraction unit 120 will be described with reference to these drawings.

(Retrieve terrain model)
First, the terrain model TM stored in the terrain model storage unit 111 is read by the terrain model reading unit 112 (Step 201: FIG. 8), and a window region for calculating the terrain amount (average inclination amount I and inclination standard deviation σ). Set. Note that the window area set here may be the same as the window area (5 × 5 in FIG. 6) used when setting the terrain classification condition, or an area having a different size may be set.

(Calculation of terrain quantity)
Next, a predetermined process (step) is repeated in order for each unit section BL constituting the target area AR (terrain model TM). First, the average inclination amount I of the unit section BL is calculated as the inclination amount by the primary tendency plane analysis (Step 202: FIG. 8). Also in this case, like the condition setting means 110, the unit partition BL to be calculated is referred to as “target unit partition BLa”, and the unit partition BL within the window area of the target unit partition BLa is referred to as “peripheral unit partition BLs”. . As described above, the average inclination amount I is obtained from the peripheral inclination amount obtained based on the target unit section BLa and the peripheral unit section BLs, and as described above, the peripheral inclination amount is calculated using the axial distance. It is obtained based on the component inclination amount.

  In addition to the average inclination amount I, the inclination standard deviation σ of the target unit section BLa is obtained by the same method as the condition setting means 110, that is, based on the peripheral inclination amount (Step 203: FIG. 8). The average inclination amount I and the inclination standard deviation σ are calculated by the terrain amount calculating means 121 shown in FIG.

(Selection of feature unit section)
When the terrain quantity of the target unit section BLa is obtained, the terrain classification condition stored in the terrain classification condition storage means 118 is read by the terrain classification condition reading means 122 shown in FIG. 9 (Step 204: FIG. 8). Then, the terrain quantity of the target unit section BLa is compared with the read terrain classification condition (that is, the threshold function) to select whether or not the target unit section BLa corresponds to the feature unit section (Step 205: FIG. 8). . Specifically, if the combination of the average inclination amount I and the inclination standard deviation σ of the target unit section BLa is on one side (upper or lower) predetermined from the threshold function, the target unit section BLa is defined as the feature unit section BLa. Is determined. This selection of feature unit sections is performed by the feature landform classification means 123 shown in FIG.

(Setting of terrain change line)
When the feature unit section is selected by the feature landform classification unit 123, an area in which a plurality of feature unit sections are gathered is generated as a feature area (Step 206: FIG. 8), and the boundary line of the feature area is a transition line or a transitional line. It is extracted as a line (Step 207: FIG. 8). The feature region is generated by the feature region generating unit 124 shown in FIG. 9, and the gradual line or the rapid line is extracted by the topographic change line setting unit 125 shown in FIG.

  The terrain classification system and the terrain classification method of the present invention can provide extremely effective clues when extracting landslide topography, deep collapse topography, and surface / shallow collapse topography on various slopes. If the invention of the present application is used, it will be useful for extraction of landslide topography, etc., and it will lead to grasp of the mechanism of landslide etc., so it will be possible to prevent natural disasters and reduce damage, and it can be used industrially. It can be said that the invention can be expected to contribute to society.

DESCRIPTION OF SYMBOLS 100 Terrain classification system of this invention 110 Condition setting means (for terrain classification system) 111 Terrain model storage means (for condition setting means) 112 Terrain model reading means (for condition setting means) 113 Sample region extraction means (for condition setting means) 114 Attention line setting means (of condition setting means) 115 Terrain amount calculation means (of condition setting means) 116 Scatter map creation means (of condition setting means) 117 Terrain classification condition setting means (of condition setting means) 118 (Condition setting means) Terrain classification condition storage means 120 (feature area extraction system) feature area extraction means 121 (feature area extraction means) terrain quantity calculation means 122 (feature area extraction means) terrain classification condition reading means 123 (feature area extraction means) ) Feature terrain classification means 124 (feature area extraction means) feature area generation means 125 (feature area extraction) Terrain change line setting means 130 (of the output means) output means (of the terrain classification system) AR predetermined area BL unit section SA sample area TM terrain data

Claims (3)

A system for classifying terrain types using a terrain model,
The terrain model is configured to include a large number of unit sections obtained by dividing a predetermined region into planes, and elevation values provided in the unit sections.
Topographical amount calculation means for obtaining an average inclination amount and an inclination standard deviation for a unit block;
Sample region extraction means for extracting a sample region by paying attention to features of the terrain among the predetermined regions by the operation of the operator;
Attention line setting means for setting an attention line that intersects the boundary of the sample region by the operation of the operator;
A scatter diagram creating means for creating a scatter diagram by arranging the average inclination amount and the inclination standard deviation of the unit section on the attention line on a biaxial plane composed of an average inclination amount axis and an inclination standard deviation axis;
Terrain classification condition setting means for setting a threshold function having the average inclination amount and the inclination standard deviation as variables based on the scatter diagram;
A feature terrain classification means for classifying a unit partition whose combination of the average slope amount and the slope standard deviation is above or below the threshold function as a feature unit partition;
With
The average amount of inclination is determined based on the amount of peripheral inclination calculated by each unit section and a plurality of unit sections around the unit section,
The inclination standard deviation is obtained based on a plurality of the peripheral inclination amounts.
A terrain classification system characterized by that. The feature unit section classified by the feature terrain classification means has a plurality of gathered areas as feature areas, and a terrain change line setting means for setting boundaries of the feature areas as transition lines or transition lines,
The terrain classification system according to claim 1, further comprising: A method of classifying terrain types using a terrain model,
The terrain model is configured to include a large number of unit sections obtained by dividing a predetermined region into planes, and elevation values provided in the unit sections.
Topographical amount calculation step for obtaining an average inclination amount and an inclination standard deviation for a unit block;
A sample area extraction step for extracting a sample area by focusing on the features of the terrain among the predetermined areas by the operation of the operator;
An attention line setting step for setting an attention line that intersects the boundary of the sample region by the operation of the operator;
A scatter diagram creating step of creating a scatter diagram by arranging the average inclination amount and the inclination standard deviation of the unit section on the attention line on a biaxial plane including an average inclination amount axis and an inclination standard deviation axis;
Based on the scatter diagram, the terrain classification condition setting step of setting a threshold function with the average inclination amount and the inclination standard deviation as variables;
A feature terrain classification step for classifying a unit block whose combination of the average slope amount and the slope standard deviation is above or below the threshold function as a feature terrain;
With
The average amount of inclination is determined based on the amount of peripheral inclination calculated by each unit section and a plurality of unit sections around the unit section,
The inclination standard deviation is obtained based on a plurality of the peripheral inclination amounts.
Topographic classification method characterized by that.

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